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| import os | |
| import webbrowser | |
| from pathlib import Path | |
| import streamlit as st | |
| import tensorflow as tf | |
| import config | |
| from src import data_preprocessing, utils | |
| MODEL_PATH = Path(config.MODEL_DIR) / config.MODEL_FILENAME | |
| VECTORIZER_PATH = Path(config.MODEL_DIR) / config.TEXT_VECTOR_FILENAME | |
| def load_model_and_vectorizer(vectorizer_path, model_path): | |
| try: | |
| text_vectorizer = utils.load_text_vectorizer(vectorizer_path) | |
| lstm_model = tf.keras.models.load_model(model_path) | |
| return text_vectorizer, lstm_model | |
| except Exception as e: | |
| return None, None | |
| def predict_sentiment(title, text, text_vectorizer, lstm_model): | |
| review = f'{title} {text}' # concatenate the title and text | |
| clean_review = data_preprocessing.clean_text(review) | |
| review_sequence = text_vectorizer([clean_review]) | |
| prediction = lstm_model.predict(review_sequence) | |
| sentiment_score = prediction[0][0] | |
| sentiment_label = 'Positive' if sentiment_score >= 0.5 else 'Negative' | |
| return sentiment_label, sentiment_score | |
| # Introduction and Project Information | |
| st.title("Amazon Review Sentiment Analysis") | |
| st.write("This is a Streamlit app for performing sentiment analysis on Amazon reviews.") | |
| st.write("Enter the title and text of the review to analyze its sentiment.") | |
| # User Inputs | |
| review_title = st.text_input("Enter the review title:") | |
| review_text = st.text_area("Enter the review text:(required)") | |
| submit = st.button("Analyze Sentiment") | |
| text_vectorizer, lstm_model = load_model_and_vectorizer( VECTORIZER_PATH, MODEL_PATH) | |
| if text_vectorizer is None or lstm_model is None: | |
| st.error('Could not load text vectorizer and model. Aborting prediction.') | |
| # Perform Sentiment Analysis | |
| if submit: | |
| with st.spinner(): | |
| sentiment_label, sentiment_score = predict_sentiment(review_title, review_text, text_vectorizer, lstm_model) | |
| new_sentiment_score= abs(0.5 - sentiment_score)*2 | |
| if sentiment_score >=0.5: | |
| st.success(f"Sentiment: {sentiment_label} (Score: {new_sentiment_score:.2f})") | |
| else: | |
| st.error(f"Sentiment: {sentiment_label} (Score: {new_sentiment_score:.2f})") | |
| # Project Usage and Links | |
| st.sidebar.write("## Project Usage") | |
| st.sidebar.write("This project performs sentiment analysis on Amazon reviews to determine whether a review's sentiment is positive or negative.") | |
| st.sidebar.write("## GitHub Repository") | |
| st.sidebar.write("Source Code here [GitHub repository](https://github.com/tikendraw/Amazon-review-sentiment-analysis).") | |
| st.sidebar.write("If you have any feedback or suggestions, feel free to open an issue or a pull request.") | |
| st.sidebar.write("## Like the Project?") | |
| st.sidebar.write("If you find this project interesting or useful, don't forget to give it a star on GitHub!") | |
| st.sidebar.markdown('', unsafe_allow_html=True) | |
| st.sidebar.write('### Created by:') | |
| c1, c2 = st.sidebar.columns([4,4]) | |
| c1.image('./src/me.jpg', width=150) | |
| c2.write('### Tikendra Kumar Sahu') | |
| st.sidebar.write('Data Science Enthusiast') | |
| githubb = st.sidebar.button('Github') | |
| linkedin = st.sidebar.button('LinkdIn') | |
| instagram = st.sidebar.button('Instagram') | |
| if githubb: | |
| webbrowser.open('https://github.com/tikendraw') | |
| if linkedin: | |
| webbrowser.open('https://www.linkedin.com/in/tikendraw/') | |
| if instagram: | |
| webbrowser.open('https://www.instagram.com/tikendraw/') | |